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Thesis Tide

Thesis Tide ranks papers based on their relevance to the fields, with the goal of making it easier to find the most relevant papers. It uses AI to analyze the content of papers and rank them!

A space with no unconditional basis that satisfies the Johnson-Lindenstrauss lemma

We give the first example of a nontrivial twisted Hilbert space that satisfies the Johnson-Lindenstrauss lemma. This space has no unconditional basis. We also show that such a space gives a partial an...

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The article presents a novel construction of a nontrivial twisted Hilbert space that satisfies the Johnson-Lindenstrauss lemma, which is significant as it adds to the understanding of high-dimensional geometry and its underlying structures. The research addresses an important mathematical question, contributing to the theoretical framework and providing potential insights for further developments in functional analysis. Its implications for the understanding of basis structures in spaces make it impactful.

What is Inside the Double-Double Structure of the Radio Galaxy J0028+0035?

The radio source J0028+0035 is a recently discovered double--double radio galaxy at redshift z=0.398. Its relic outer lobes are separated by about 3' in the sky, corresponding to ~1 Mpc projected ...

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The article presents novel findings on the double-double structure of a radio galaxy, offering insights into the temporal evolution of radio activity. The use of advanced observational techniques enhances the methodological rigor. Furthermore, the potential identification of a triple-double source, although not confirmed, provokes intriguing questions about radio galaxies' formation and behavior, stimulating further research in astrophysics and related fields.

Fractional Divisibility of Spheres with Partially Generic Sets of Rotations

We say that an r-tuple $(g_1,...,g_r)$ of special orthogonal $d\times d$ matrices fractionally divides the $(d-1)$ -dimensional sphere $S$ if there is a non-constant fun...

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The article presents a novel approach to understanding the properties of spheres under the action of rotations, particularly through the concept of fractional divisibility. The mathematical rigor and abstraction involved in exploring generic rotations extend the existing body of knowledge in geometric functional analysis and representation theory. The implications of these findings could inspire further research in both theoretical and applied mathematics, particularly in areas involving symmetry and transformations.

Navigating permanent underdetermination in dark energy and inflationary cosmology

We identify troubling cases of so-called `permanent underdetermination' in both dark energy and inflationary cosmology. We bring to bear (a) a taxonomy of possible responses to underdetermina...

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The article addresses a significant epistemological issue in cosmology, specifically related to dark energy and inflation, which are fundamental to our understanding of the universe's expansion and early conditions. Its novel approach of combining effective field theory with a taxonomy of responses to underdetermination is a substantial contribution that could inspire further theoretical work in this area. The exploration of potential responses to underdetermination promotes methodological rigor and opens avenues for more concrete theoretical development, thus enhancing its relevance. Although it may have a niche focus, the clarity in discussing the perpetual challenge of underdetermination in these disciplines adds value to cosmology.

Network Centrality as a New Perspective on Microservice Architecture

Context: Over the past decade, the adoption of Microservice Architecture (MSA) has led to the identification of various patterns and anti-patterns, such as Nano/Mega/Hub services. Detecting these anti...

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This article presents a novel approach to evaluating Microservice Architecture (MSA) by integrating network centrality metrics into the assessment. The use of rigorous empirical analysis of open-source MSA projects is commendable, and the correlation findings provide meaningful insights that extend existing methodologies. The potential application of network centrality for detecting architectural anti-patterns could significantly influence future research agendas. While the findings are promising, further exploration of some centrality metrics' applicability is needed, preventing a perfect score.

Power integral bases in a family of octic fields

Several recent results prove the monogenity of some polynomials. In these cases the root of the polynomial generates a power integral basis in the number field generated by the root. A straightforward...

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The article addresses an advanced topic in algebraic number theory, particularly focusing on the concept of power integral bases within octic fields. The novelty lies in the extension of previous results and the investigation of whether other generators exist for these bases. The rigorous approach and specificity of the polynomials analyzed add significant value, although its application may be limited to a niche audience within mathematical research.

Text-driven Online Action Detection

Detecting actions as they occur is essential for applications like video surveillance, autonomous driving, and human-robot interaction. Known as online action detection, this task requires classifying...

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The article presents a novel architecture (TOAD) that effectively integrates language and vision modalities for online action detection, tackling significant challenges such as computational efficiency and robustness against incomplete actions. Its performance benchmarks indicate clear advancements over existing techniques, suggesting substantial implications for various applications in real-time action detection.

Propensity-driven Uncertainty Learning for Sample Exploration in Source-Free Active Domain Adaptation

Source-free active domain adaptation (SFADA) addresses the challenge of adapting a pre-trained model to new domains without access to source data while minimizing the need for target domain annotation...

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The article addresses a significant challenge in machine learning: adapting models to new domains in a source-free setting. Its novel approach to sample selection and uncertainty learning could substantially enhance performance in scenarios where traditional methods falter, particularly in the context of data limitations such as privacy concerns. The methodological rigor is backed by extensive experiments that show its superiority over existing techniques, indicating high applicability and relevance.

Communication-Efficient Stochastic Distributed Learning

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multi...

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The article presents a novel approach to communication-efficient distributed learning using a robust algorithm that balances local computation with communications, thus addressing critical challenges in scalable machine learning. Its methodological rigor is evident in the convergence guarantees provided for both convex and nonconvex problems. The comprehensive comparisons with state-of-the-art methods enhance its impact, suggesting a strong potential for future research applications in this domain.

Structural schemes for hamiltonian systems

We present an adaptation of the so-called structural method \cite{CMM23} for Hamiltonian systems, and redesign the method for this specific context, which involves two coupled differential systems. St...

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The article introduces a novel approach adapted for Hamiltonian systems, which is significant due to the importance of Hamiltonian dynamics in both theoretical and applied physics. The emphasis on structural schemes that ensure stability and high-order accuracy contributes to methodological advancements. The detailed extension from scalar to vector cases and the consideration of non-separable systems showcases robustness in its applicability. Furthermore, comparisons with traditional methods provide valuable context for its potential impact.

Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement

Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a ``microscope'' for anatomical structures and routinely mitigates the influence of low signal-to-noise ra...

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This article presents a novel self-supervised denoising method specifically tailored for diffusion MRI, which is a critical area in medical imaging. The methodology offers significant improvements in efficiency and stability over existing techniques, which directly addresses major clinical shortcomings. Its applicability in real-world scenarios for improving microstructure modeling and tractography tracking enhances its relevance, indicating potential for wide adoption and further research development.

A Parallel Block Preconditioner-Based VIE-FFT Algorithm for Modeling the Electromagnetic Response From Nanostructures

The superior ability of nanostructures to manipulate light has propelled extensive applications in nano-electromagnetic components and devices. Computational electromagnetics plays a critical role in ...

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The article presents a novel computational approach to modeling electromagnetic responses in nanostructures, an area of increasing relevance due to technological advancements in nanophotonics. Its methodological rigor is highlighted by the combination of VIE, FFT, and parallel computation techniques, making it highly applicable for complex nanostructures. Its effectiveness demonstrated through numerical experiments adds to its impact.

Signature of superconductivity in pressurized La4Ni3O10-x single crystals grown at ambient pressure

Nickelates have attracted enormous attention since the discovery of high-temperature superconductivity in La3Ni2O7 under high pressure. However, whether superconducting nickelate single crystals can b...

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The article presents a significant advancement in the field of superconductivity by demonstrating the preparation and properties of high-quality La4Ni3O10-x single crystals that exhibit superconductivity under high pressure. Its novelty lies in the successful growth of these nickelate crystals at ambient pressure, which could revolutionize studies on high-temperature superconductivity and expand research methodologies. The methodological rigor is evident through advanced characterization techniques such as X-ray diffraction and electron microscopy, making the findings robust and credible.

Categorical quotients for actions of groupoids on varieties

For certain actions of the Weyl groupoid $\mathfrak{W}$ from [Sergeev and Veselov, Grothendieck rings of basic classical Lie superalgebras, Ann Math, 2011] on an affine variety $X$ ...

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The article presents novel insights into the geometric properties of maps derived from groupoid actions on affine varieties, making it a significant contribution to the understanding of actions of mathematical constructs on geometric objects. The methodological rigor shown in adapting classical results to a modern context enhances its impact, particularly as it extends to uncountable base fields, which could have implications in various algebraic contexts.

Model category structures on truncated multicomplexes for complex geometry

To a bicomplex one can associate two natural filtrations, the column and row filtrations, and then two associated spectral sequences. This can be generalized to $N$ -multicomplexes. We present ...

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This article offers a novel approach to modeling category structures that directly impacts the study of complex geometry, particularly through its introduction of spectral sequences in the context of multicomplexes. The methodological rigor is evident in the detailed exposition of weak equivalences, providing a solid foundation for future research into homotopy types of complex manifolds. The interdisciplinary nature of the work suggests potential applications beyond theoretical mathematics, making it broadly relevant.

IoT Performance for Maritime Passenger Evacuation

The safe and swift evacuation of passengers from Maritime Vessels, requires an effective Internet of Things(IoT) as well as an information and communication technology(ICT) infrastructure. However, du...

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The article addresses a significant and timely challenge in maritime safety, focusing on the critical role of IoT and ICT systems during passenger evacuations. The methodology appears innovative, utilizing a simulation-based approach to analyze evacuation scenarios and emphasizing the importance of timely information delivery. Its findings have practical implications for improving safety protocols in maritime contexts, pointing toward actionable insights for enhancing systems during emergencies.

Iterative Shaping of Multi-Particle Aggregates based on Action Trees and VLM

In this paper, we address the problem of manipulating multi-particle aggregates using a bimanual robotic system. Our approach enables the autonomous transport of dispersed particles through a series o...

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This article presents a novel approach in robotic manipulation by integrating Vision Language Models with advanced trajectory execution techniques. The use of high-level task planning alongside geometric representation for trajectory optimization is a significant contribution to the field. The practical demonstrations in real-world settings show methodological rigor and applicability, thus indicating a strong potential for real-world applications and inspiring further research.

Inverted finite elements approximation of the Neumann problem for second order elliptic equations in exterior two-dimensional domains

We use inverted finite elements method for approximating solutions of second order elliptic equations with non-constant coefficients varying to infinity in the exterior of a 2D bounded obstacle, when ...

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This article presents a novel approach using inverted finite elements method, which could significantly enhance computational techniques in dealing with Neumann boundary problems. The combination of rigorous analysis and numerical confirmation of convergence indicates both methodological rigor and practical applicability, suggesting potential improvements in solving complex boundary value problems.

GRB 221009A and the Apparently Most Energetic Gamma-Ray Bursts

Gamma-Ray Bursts (GRBs) are often referred to as the most luminous explosions in the Universe, due to their short and highly luminous prompt emission. This apparent luminosity, however, does not refle...

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The study introduces an updated and expansive sample of Gamma-Ray Bursts (GRBs) to analyze the isotropic equivalent energy distribution, providing new insights into the energetics and geometry of GRB jets. The inclusion of GRB 221009A, the most energetic GRB to date, signifies a substantial advancement in understanding the upper limits of GRB energies. The methodologies employed, notably in evaluating different distribution models, highlight the methodological rigor and the paper’s contribution to resolving questions about GRB energy budgets. Its implications have the potential to spur further research into jet formation and energy dispersion in the universe.

Continuous signal sparse encoding using analog neuromorphic variability

Achieving fast and reliable temporal signal encoding is crucial for low-power, always-on systems. While current spike-based encoding algorithms rely on complex networks or precise timing references, s...

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The article presents a novel encoding framework that leverages biological principles and intrinsic neuronal variability, innovating in the field of neuromorphic engineering. Its practical application to analog hardware and robust performance under various conditions enhance its relevance. The low complexity of the model facilitates implementation in real-world settings, which is a significant strength. Overall, the methodological rigor of the validation processes in both simulation and hardware showcases its potential impact on future research and technological developments.